Artificial intelligence-based interference recognition method for electrocardiogram

ABSTRACT

An artificial intelligence-based interference recognition method for an electrocardiogram, comprising: cutting and sampling heart beat data of a first data amount, and inputting the heart beat data to be recognized that is obtained by cutting and sampling into an interference recognition binary classification model for interference recognition; in a sequence of the heart beat data, performing signal anomaly determination on a heart beat data segment where an inter-beat interval is greater than or equal to a preset interval determination threshold value, so as to determine whether the heart beat data segment is an abnormal signal; if the heart beat data segment is not an abnormal signal, determining a starting data point and an ending data point of sliding sampling in the heart beat data segment according to a set time with a preset time width, and performing sliding sampling on the data segment from the starting data point until the ending data point so as to obtain multiple sampling data segments; and using each sampling data segment as heart beat data to be recognized for interference recognition.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a national phase entry under 35 U.S.C. § 371 ofInternational Patent Application PCT/CN2018/072349, filed Jan. 12, 2018,designating the United States of America and published as InternationalPatent Publication WO 2019/100561 A1 on May 31, 2019, and claimspriority to Chinese Patent Application, filed to the Chinese PatentOffice on Nov. 27, 2017, Application No. 201711203069.4, entitled“Artificial intelligence-based interference identification method forelectrocardiogram.”

TECHNICAL FIELD

The present disclosure relates to the technical field of artificialintelligence data analysis and processing, and more particularly, to anartificial intelligence-based interference identification method forelectrocardiogram.

BACKGROUND

In 1908, Einthoven began to use electrocardiography (ECG) to monitorelectrophysiological activities of a heart. At present, noninvasive ECGexamination has become one of the important methods for diagnosis andscreening heart diseases in clinical cardiovascular field. ECGexamination can be divided into several categories such as resting ECG,ambulatory ECG and exercise ECG according to clinical application.

ECG monitoring is an important measure for observation, diagnosis andtreatment of cardiovascular patients, and can monitor whether there isarrhythmia, a frequency of heart beat in real time, and thus, timely andeffective measures can be taken according to ECG activities. Althoughmost of ambulatory ECG analysis software in the market can automaticallyanalyze data, in clinical work, ECG detection and recording process aresusceptible to interference caused by various influences, resulting ininvalid or inaccurate acquired data, which cannot correctly reflectcondition of patients and increases difficulty and workload for doctorsin diagnosis. Meanwhile, interference data is also a main factor thatmakes intelligent diagnostic tools unable to work effectively.Therefore, it is particularly important to minimize interference.

BRIEF SUMMARY

The purpose of the present disclosure is to provide an artificialintelligence-based interference identification method forelectrocardiogram. An end-to-end two-classification identificationsystem with deep learning algorithm as its core has characteristics ofhigh precision and strong generalization performance, and it caneffectively solve disturbance problems caused by main disturbancesources such as electrode falling off, motion interference and staticinterference, and thus, the problem of poor identification caused byvarious and irregular disturbance data in traditional algorithms isovercome.

To achieve the above purpose, the present disclosure provides anartificial intelligence-based interference identification method forelectrocardiogram, including:

-   -   performing cutting and sampling on heart beat data with a first        data amount, and inputting heart beat data to be identified        obtained by the cutting and sampling into an interference        identification two-classification model to identify        interference;    -   determining a heart beat data segment with a heart beat interval        greater than or equal to a preset interval determination        threshold in a sequence of the heart beat data to be identified;    -   performing judgment of signal abnormality on the heart beat data        segment with the heart beat interval greater than or equal to        the preset interval determination threshold to determine whether        the heart beat data segment is an abnormal signal;    -   if the heart beat data segment is not the abnormal signal,        according to a set time value, determining a starting data point        and an ending data point for sliding sampling in the heart beat        data segment with a preset time width, and performing the        sliding sampling on the data segment from the starting data        point to the ending data point to obtain multiple sample data        segments; and    -   taking each of the sample data segments as the heart beat data        to be identified and performing the interference identification        method.

Preferably, the performing cutting and sampling on heart beat data witha first data amount specifically includes:

-   -   determining a sample midpoint of the heart beat data; and    -   taking the sample midpoint as a center, according to a time        sequence of the heart beat data, performing data interception        from the sample midpoint to two ends to obtain the first data        amount of sample data.

Further preferably, the sample midpoint of the heart beat data is a Rpoint of QRS wave complex data in the heart beat data.

Further preferably, the data interception includes:

-   -   interception according to a number of data points or according        to a length of a time period.

Preferably, the heart beat data is single-lead or multi-lead heart beatdata, and the performing cutting and sampling on heart beat data with afirst data amount includes:

-   -   determining a sample midpoint of the single-lead or multi-lead        heart beat data; and    -   performing the cutting and sampling based on the sample midpoint        of the single-lead or multi-lead heart beat data with the first        data amount.

Preferably, the inputting the heart beat data to be identified obtainedby the cutting and sampling into an interference identificationtwo-classification model to identify interference includes:

-   -   determining an interference noise probability value of the heart        beat data to be identified of single-lead or multi-lead        according to the interference identification two-classification        model; and    -   determining whether the heart beat data to be identified is        interference data or non-interference data according to the        interference noise probability value.

Further preferably, the method further includes: labeling theinterference data.

Preferably, the method further includes: establishing the interferenceidentification two-classification model based on artificial intelligenceself-learning training.

Further preferably, the training includes:

-   -   labeling training data;    -   performing data format conversion and storage on the training        data, and converting the data format into a preset standard data        format; and    -   performing training according to the training data in the preset        standard data format.

The artificial intelligence-based interference identification method forelectrocardiogram provided by the embodiments of the disclosureconstructs an end-to-end two-classification identification system takingdeep learning algorithm as a core, which has characteristics of highprecision and strong generalization performance, and it can effectivelysolve disturbances generated by main disturbance sources such aselectrode peeling off, motion interference and static interference. Themethod adopts an off-line trained deep learning model to classify inputheart beat data, and a classification result of whether the heart beatdata is interference or not is directly output. The result is obtainedquickly, the identification accuracy is high, the stability performanceis good, and effective and high-quality data can be provided forsubsequent analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating an artificial intelligence-basedinterference identification method for electrocardiogram according to anembodiment of the present disclosure;

FIG. 3 is a graphical representation of a QRS wave complex during theartificial intelligence-based interference identification method of FIG.1; and

FIG. 2 is a schematic diagram illustrating an interferenceidentification two-classification model according to an embodiment ofthe present disclosure.

DETAILED DESCRIPTION

Technical solutions of the present disclosure will be further describedin detail below through accompanying drawings and embodiments.

In order to facilitate understanding of the technical solutions of thepresent disclosure, basic principles of artificial intelligence models,especially convolutional neural network models, are first introduced.

Artificial intelligence Convolutional Neural Network (CNN) model is asupervised learning method in deep learning, which is a multi-layernetwork (hidden layer) connection structure that simulates a neuralnetwork. An input signal sequentially passes through each hidden layer,in which a series of complex mathematical processes (Convolution,Pooling, Regularization, prevention of over-fitting, Dropout,Activation, and general use of Rectified Linear Unit activationfunction) are carried out. Some features of an object to be identifiedare automatically abstracted layer by layer, these features aretransmitted as input to a higher hidden layer for calculation until anentire signal is reconstructed by the last several full connectionlayers, and Softmax function is used to perform logistics regression toachieve multi-objective classification.

CNN belongs to the supervised learning method in artificialintelligence. In a training phase, the input signal is processed throughmultiple hidden layers to reach last full connection layers. There is anerror between a classification result obtained by Softmax logicalregression and a known classification result (label). One of core ideasof deep learning is to continuously minimize the error through a largenumber of sample iterations so as to calculate and obtain parameters forconnecting neurons in each hidden layer. In this process, it isgenerally necessary to construct a special cost function, and quicklyand effectively minimize all connection parameters in a neural networkstructure with complex depth (number of hidden layers) and breadth(dimension of features) by using a nonlinearly optimized gradientdescent algorithm and an error back propagation (BP) algorithm.

In deep learning, data needed to be identified is input into a trainingmodel, and finally an identification result is output after the datapasses through a first hidden layer, a second hidden layer and a thirdhidden layer. Features with different degrees of abstraction areextracted in each layer, and finally specific categories of originaldata are identified, such as cars, people or animals.

An algorithm model of deep learning is very complex in mathematics.Developing a complete algorithm program requires strong professionalbackground knowledge and rich work experience. In recent years,companies such as GOOGLE®, Microsoft, Baidu, Facebook and some famousuniversities (such as University of California, Berkeley, and Universityof Montreal in Canada) have also successively developed and launchedopen source platforms for artificial intelligence development withdifferent characteristics, helping some research and developmentcompanies in the field of deep learning to quickly master thiscutting-edge technology. Among them, Caffe of Berkeley and Tensorflow ofGOOGLE® are currently the two most widely used framework tools.

The model of deep learning is extremely complex, and training dataneeded is from hundreds of thousands, millions to tens of millions,coupled with repeated loop iterations, resulting in a very large amountof nonlinear optimized calculation. For an actual project, it oftentakes from a dozen hours to several days or even longer to calculate byusing a central processing unit of a common computer. In this case,Graphics Processing Unit (GPU) replaces it to greatly speed up thecalculation. At present, GPU cards provided by Nvidia company, due topowerful graphics and computer vision computing capabilities, a largenumber of computing database such as linear algebra, and supporting ofparallel processing, can meet the computing of various methods with deeplearning needs, and becomes a basic hardware for high-performancetraining and inference of current artificial intelligence.

The artificial intelligence-based interference identification method forelectrocardiogram of the present disclosure is implemented based on theCNN model.

The flowchart of the artificial intelligence-based interferenceidentification method for electrocardiogram shown in FIG. 1 belowillustrates specific implementation of the technical solutions of thepresent disclosure.

As shown in FIG. 1, the artificial intelligence-based interferenceidentification method for electrocardiogram provided by the presentdisclosure includes as follows.

Step 110, further illustrated in FIG. 3, cutting and sampling areperformed on heart beat data 302 with a first data amount, and heartbeat data to be identified obtained by cutting and sampling is inputinto an interference identification two-classification model to identifyinterference.

Firstly, a sample midpoint 306 of the heart beat data 302 is determined,and specifically, a R point of QRS wave complex data in the heart beatdata may be selected. Then, taking the sample midpoint 306 as a center,according to a time sequence of the heart beat data, data interceptionis performed from the sample midpoint 306 to two ends, and the firstdata amount of sample data 304 is obtained. Data interception may beperformed according to a number of data points or according to a lengthof a time period.

The above cutting and sampling may be for single-lead or multi-lead. Inthe case of multi-lead, the sample midpoint 306 of the heart beat data302 of each lead can be determined separately for each lead, and thencutting and sampling are performed based on the sample midpoint 306 ofthe heart beat data 302 of each lead with the first data amount. Inother words, a same amount of data is intercepted forward and backwardfrom the sample midpoint 306. Interception can be specifically carriedout according to a set number of the data points or according to a setlength of the time period.

In the present disclosure, the specific implementation solution of thatto identify interference may be to determine an interference noiseprobability value of the heart beat data to be identified for each leadaccording to the interference identification two-classification model,and then determine whether the heart beat data to be identified isinterference data or non-interference data according to the interferencenoise probability value.

Training of the interference identification two-classification modelwill be described in detail in the following.

Step 120, a heartbeat data segment with a heartbeat interval greaterthan or equal to a preset interval determination threshold in thesequence of the heart beat data to be identified is determined.

Specifically, the preset interval may preferably be 2 seconds. If theheart beat interval is greater than or equal to the preset interval, itindicates that there may be signal abnormality, so it is necessary toperform a judgment of signal abnormality first.

Step 130, the judgment of signal abnormality is performed on the heartbeat data segment with the heart beat interval greater than or equal tothe preset interval determination threshold to determine whether theheart beat data segment is an abnormal signal.

Specifically, the judgment of signal abnormality includes the judgmentof signal overflow, low voltage, electrode peeling off, etc. For theheart beat data with the heart beat interval greater than or equal to 2seconds, whether it is signal overflow, low voltage or electrode peelingoff is first judged with the interference identificationtwo-classification model.

If it is not an abnormal signal, step 140 is executed. If it is anabnormal signal, step 150 is executed.

Step 140, according to a set time value, a starting data point and anending data point for sliding sampling in the heart beat data segmentare determined with a preset time width, and sliding sampling isperformed on the data segment from the starting data point to the endingdata point to obtain multiple sample data segments.

In other words, according to the set time value, the starting data pointof a first sample data segment of the heart beat data in the foremost oftime sequence is determined, and then non-overlapped sliding samplingwith the preset time width is performed backward continuously accordingto the present time width. Preferably, the number of data pointsincluded in each sample data segment is also the first data amount.

Then, step 160 is executed.

Step 150, the abnormal signal is labeled, returns to step 120, and nextheart beat data segment with heart beat interval greater than or equalto the preset interval determination threshold continues to beidentified.

Step 160, each of the sample data segments is taken as the heart beatdata to be identified and the interference identification method isperformed.

Further, the interference data that is identified is labeled.

The above-mentioned structure of the interference identificationtwo-classification model is an end-to-end two-classificationidentification system inspired and constructed by artificialintelligence deep learning CNN models such as LeNet-5 and AlexNet.

For the training of the model, nearly 4 million accurately labeled datasegments from 300,000 patients are used. Labeling is divided into twocategories: normal ECG signals or ECG signal fragments with obviousinterference. The segments are labeled by custom-developed tools, andthen interference fragment information is saved in a customized standarddata format.

In the training process, two GPU servers are used for dozens ofround-robin training. In a specific example, for a segment D [300] witha sample rate of 200 Hz and a data length of 300 ECG voltage values(millivolts), input data is: InputData (i, j), wherein i is a i-th lead,and j is a j-th segment of the i-th lead. All input data is randomlyscattered before training, which ensures convergence of the trainingprocess. At the same time, collection of too many samples from the ECGdata of a same patient is controlled, improving the generalizationability of the model, that is, an accuracy rate in a real scene. Afterthe training converges, one million pieces of independent test data areused for testing, and the accuracy rate can reach 99.3%. Additionally,specific test data is shown in Table 1 below.

TABLE 1 Interference Normal Sensitivity 99.14% 99.32% PositivePredictivity 96.44% 99.84%

Interference data is often caused by external disturbance factors,mainly including electrode peeling off, low voltage, electrostaticinterference and motion interference. Not only interference datagenerated by different disturbance sources is different, but alsointerference data generated by a same disturbance source is diverse. Atthe same time, considering that although the diversity of interferencedata is widely distributed, the difference with normal data is verylarge, so the diversity is ensured as much as possible when collectinginterference training data. Furthermore, moving window sliding samplingis adopted to increase the diversity of interference data as much aspossible, so as to make the model robust to interference data. Even ifinterference data in the future is different from any previousinterference, with comparison to normal data, its similarity withinterference is greater than normal data, thus enhancing the ability ofthe model to identify interference data.

The interference identification two-classification model adopted in thisstep can be shown in FIG. 2. The network first uses two convolutionallayers, the convolution kernel in size is 1×5, and each layer isfollowed by a maximum pooling. The number of the convolution kernelstarts from 128, and the number of the convolution kernel doubles everytime passing a maximum pooling layer. The convolutional layers arefollowed by two full connection layers and a Softmax classifier. Sincethe classification number of the model is two, Softmax has two outputunits that correspond to corresponding categories in turn, and usescross entropy as the cost function.

The artificial intelligence-based interference identification method forelectrocardiogram provided by the embodiments of the disclosureconstructs an end-to-end two-classification identification system takingdeep learning algorithm as a core, which has characteristics of highprecision and strong generalization performance, and can effectivelysolve disturbances generated by main disturbance sources such aselectrode peeling off, motion interference and static interference. Themethod adopts an off-line trained deep learning model to classify inputheart beat data, and a classification result of whether the heart beatdata is interference or not is directly output. The result is obtainedquickly, the identification accuracy is high, the stability performanceis good, and effective and high-quality data can be provided forsubsequent analysis.

Those skilled in the art should further realize that the units andalgorithm steps of the examples described in the embodiments disclosedherein can be implemented in electronic hardware, computer software, ora combination of the two. In order to clearly illustrate theinterchangeability of hardware and software, the composition and stepsof each example have been generally described according to functions inthe above description. Whether these functions are implemented inhardware or software depends on the specific application and designconstraints of the technical solutions. Those skilled in the art may usedifferent methods to implement the described functions for each specificapplication, but such implementation should not be considered to bebeyond the scope of the present disclosure.

The steps of methods or algorithm described in the embodiments disclosedherein may be implemented in hardware, a software module executed by aprocessor, or a combination of the two. The software module may beplaced in random access memory (RAM), memory, read only memory (ROM),electrically programmable ROM, electrically erasable programmable ROM,registers, hard disks, removable disks, CD-ROM, or any other form ofstorage medium known in the technical field.

The specific embodiments described above have further explained thepurpose, technical solution and beneficial effects of the presentdisclosure in detail. It should be understood that the above is onlyspecific embodiments of the present disclosure and is not used to limitthe scope of protection of the present disclosure. Any modification,equivalent substitution, improvement, etc., made within the spirit andprinciples of the present disclosure should be included in the scope ofprotection of the present disclosure.

What is claimed is:
 1. An artificial intelligence-based interferenceidentification method for electrocardiogram, comprising: performingcutting and sampling on heart beat data with a first data amount, andinputting heart beat data obtained by the cutting and sampling into aninterference identification two-classification model to identifyinterference; determining a heart beat data segment with a heart beatinterval greater than or equal to a preset interval determinationthreshold in a sequence of the result of the cutting and sampling onheart beat data; performing judgment of signal abnormality on the heartbeat data segment with the heart beat interval greater than or equal tothe preset interval determination threshold to determine whether theheart beat data segment is an abnormal signal; if the heart beat datasegment is the abnormal signal, labeling it, and continue determiningthe next heart beat data segment with a heart beat interval greater thanor equal to a preset interval determination threshold in a sequence ofthe result of the cutting and sampling on heart beat data; if the heartbeat data segment is not the abnormal signal, according to a set timevalue, determining a starting data point and an ending data point forsliding sampling in the heart beat data segment with a preset timewidth, and performing the sliding sampling on the data segment from thestarting data point to the ending data point to obtain multiple sampledata segments; and taking each of the sample data segments of the heartbeat data into an interference identification two-classification modelto identify interference, and performing the interference identificationmethod; wherein the performing cutting and sampling on heart beat datawith the first data amount comprises: determining an interference noiseprobability value of the heart beat data of single-lead or multi-leadaccording to the interference identification two-classification model;and determining whether the heart beat data is interference data ornon-interference data according to the interference noise probabilityvalue.
 2. The method according to claim 1, wherein the performingcutting and sampling on heart beat data with a first data amountcomprises: determining a sample midpoint of the heart beat data; andtaking the sample midpoint as a center, according to a time sequence ofthe heart beat data, performing data interception from the samplemidpoint to two ends to obtain the first data amount of sample data. 3.The method according to claim 2, wherein the sample midpoint of theheart beat data is a R point of QRS wave complex data in the heart beatdata.
 4. The method according to claim 2, wherein the data interceptioncomprises: interception according to a number of data points oraccording to a length of a time period.
 5. The method according to claim1, wherein the heart beat data is single-lead or multi-lead heart beatdata, and the performing cutting and sampling on heart beat data with afirst data amount comprises: determining a sample midpoint of thesingle-lead or multi-lead heart beat data; and performing the cuttingand sampling based on the sample midpoint of the single-lead ormulti-lead heart beat data with the first data amount.
 6. The methodaccording to claim 1, further comprising: labeling the interferencedata.
 7. The method according to claim 1, further comprising:establishing the interference identification two-classification modelbased on artificial intelligence self-learning training.
 8. The methodaccording to claim 7, wherein the training comprises: labeling trainingdata; performing data format conversion and storage on the trainingdata, and converting the data format into a preset standard data format;and performing training according to the training data in the presetstandard data format.